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Nobody
Hannes Nickisch
Dr.
Position: Research Scientist
Room no.: 222
Phone: +49 7071 601 584
Fax: +49 7071 601 552

My research is focussed on approximate inference and estimation in generalised linear models such as Gaussian processes and sparse linear models. Applications include MRI sequence design and image reconstruction as well as density estimation and classification.
For details, visit my homepage.

JOB
since 03/11: Philips Research Laboratories Hamburg
Research Scientist

STUDY/EDUCATION
10/10 – 03/11: Max-Planck-Institute for Biological Cybernetics
Department: Empirical Inference for Machine Learning and Perception
PostDoc

10/06 – 09/10: Max-Planck-Institute for Biological Cybernetics
Department: Empirical Inference for Machine Learning and Perception
Ph.D. student

10/04 – 09/06: Berlin University of Technology
Dual Degree of Computer Science (Maîtrise & Diplom)
Major: Artificial Intelligence, Neural Information Processing
Minor: Statistics, Pattern Recognition and Image Processing
Diploma Thesis: “Extraction of visual features from natural video data using Slow Feature Analysis”

09/03 – 06/04: Université de Nantes (France)
Maîtrise d’Informatique (1st year of Master) funded by a DAAD-scholarship
Majors: Artificial Intelligence, Language and Image Processing

10/01 – 08/03: Berlin University of Technology
Vordiplom of Computer Science
Minor: Cognitive Science

WORKING EXPERIENCE AND FURTHER EDUCATION
07/09 – 09/09: Microsoft Corporate Research, Cambridge, UK
Summer student in the Computervision Group
Topic: Interactive Segmentation

07/05 – 10/05: Siemens Corporate Research, Princeton, US
Summer student in the Imaging and Visualization Department
Evaluation and implementation of probabilistic inference on images
Topic: Nonparametric Belief Propagation

10/04 – 09/06: Berlin University of Technology
Student assistant in a project of the German Research Foundation
Neurobiologically inspired controller architecture for mobile robots
Feature extraction from video data (Optical Flow, Slow Feature Analysis)

07/04 – 09/04: Siemens Medical Solutions, Erlangen
Summer student at Magnetic Resonance/Development/Application
Implementation of an image processing algorithm on MR-T1 images
Topic: Skull Stripping (Extraction of brain matter from 3D datasets)

10/02 – 06/03: Berlin University of Technology
Student assistant in the Neural Information Processing Group
Project in the field of Computational Neuroscience:
Contrast adaptation in an orientation column in the visual cortex (V1)


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2014
Articles
Posters
  • M. Babayeva, A. Loktyushin, T. Kober, C. Granziera, H. Nickisch, R. Gruetter, G. Krueger (2014). FID-guided retrospective motion correction based on autofocusing Joint Annual Meeting ISMRM-ESMRMB 2014, Milano, Italy
2013
Articles
2012
Articles
  • H. Nickisch (2012). glm-ie: The Generalised Linear Models Inference and Estimation Toolbox Journal of Machine Learning Research, 13, 1699-1703
Posters
2011
Articles
Conference Papers
  • M. Seeger, H. Nickisch (2011). Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference In: JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011, (Ed) Gordon, G. , D. Dunson, M. Dudík , MIT Press, Cambridge, MA, USA, 652-660, 14th International Conference on Artificial Intelligence and Statistics
  • D. Duvenaud, H. Nickisch, CA. Rasmussen (2011). Additive Gaussian Processes In: Advances in Neural Information Processing Systems 24, (Ed) J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger, 226-234, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
Posters
  • A. Loktyushin, H. Nickisch, R. Pohmann (2011). Retrospective blind motion correction of MR images Magnetic Resonance Materials in Physics, Biology and Medicine, 24, (Supplement 1), 498, 28th Annual Scientific Meeting ESMRMB 2011
Technical Reports
2010
Articles
Conference Papers
  • H. Nickisch, CE. Rasmussen (2010). Gaussian Mixture Modeling with Gaussian Process Latent Variable Models In: Pattern Recognition, (Ed) Goesele, M. , S. Roth, A. Kuijper, B. Schiele, K. Schindler, Pattern Recognition: 32nd DAGM Symposium, Springer, Deutsche Arbeitsgemeinschaft für Mustererkennung, Berlin, Germany, 271-282, ISBN: 978-3-642-15986-2, 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM 2010)
  • H. Nickisch, C. Rother, P. Kohli, C. Rhemann (2010). Learning an interactive segmentation system (Ed) Chellapa, R. , P. Anandan, A. N. Rajagopalan, P. J. Narayanan, P. Torr, Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2010), ACM Press, Nw York, NY, USA, 274-281, ISBN: 978-1-4503-0060-5, Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2010)
Theses
  • H. Nickisch (2010). Bayesian Inference and Experimental Design for Large Generalised Linear Models Technische Universität Berlin, Berlin, Germany
Technical Reports
  • M. Seeger, H. Nickisch (2010). Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models Max Planck Institute for Biological Cybernetics
2009
Conference Papers
  • H. Nickisch, MW. Seeger (2009). Convex variational Bayesian inference for large scale generalized linear models In: ICML 2009, (Ed) Danyluk, A. , L. Bottou, M. Littman, Proceedings of the 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 761-768, 26th International Conference on Machine Learning
  • MW. Seeger, H. Nickisch, R. Pohmann, B. Schölkopf (2009). Bayesian Experimental Design of Magnetic Resonance Imaging Sequences In: Advances in neural information processing systems 21, (Ed) D Koller and D Schuurmans and Y Bengio and L Bottou, Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008, Curran, Red Hook, NY, USA, 1441-1448, ISBN: 978-1-605-60949-2, 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008)
  • Lampert, CH. and Nickisch, H. and Harmeling, S. (2009). Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer In: CVPR 2009, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), IEEE Service Center, Piscataway, NJ, USA, 951-958, IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Technical Reports
  • H. Nickisch, P. Kohli, C. Rother (2009). Learning an Interactive Segmentation System Max Planck Institute for Biological Cybernetics
Posters
2008
Articles
Conference Papers
  • MW. Seeger, H. Nickisch (2008). Compressed Sensing and Bayesian Experimental Design In: ICML 2008, (Ed) Cohen, W. W., A. McCallum, S. Roweis, Proceedings of the 25th International Conference on Machine Learning (ICML 2008), ACM Press, New York, NY, USA, 912-919, 25th International Conference on Machine Learning
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Technical Reports
  • MW. Seeger, H. Nickisch (2008). Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
2006
Theses
  • Nickisch, H. (2006). Extraction of visual features from natural video data using Slow Feature Analysis Technische Universität Berlin, Berlin, Germany